A machine learning approach where models are trained across many devices or servers without moving the raw data to a central location.
In traditional machine learning, data is collected in one place for training. Federated learning flips this: a shared model is sent to devices, each device trains it locally on its own data, and only the resulting model updates are sent back and combined.
A well-known use is smartphone keyboards that improve next-word suggestions from typing patterns without uploading what people actually type. Hospitals can also collaborate on a diagnostic model without sharing patient records directly. Federated learning improves privacy but is not a complete guarantee, because model updates can sometimes leak information. It is often paired with techniques such as differential privacy and secure aggregation, and it must cope with uneven, unreliable devices.
Processing data close to where it is generated, such as on devices or local servers, instead of sending everything to a distant data center.
GDPRGeneral Data Protection Regulation: the European Union's law governing how organizations collect, use, and protect personal data.
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